CIRCULATE Galaxy cohort B Ando et al 2024
library(swimplot) library(coxphf) library(grid) library(gtable) library(readr) library(mosaic) library(dplyr) library(officer) library(flextable) library(survival) library(survminer) library(gridtext) library(ggplot2) library(scales) library(ggthemes) library(tidyverse) library(gtsummary) library(flextable) library(parameters) library(car) library(grid) library(ComplexHeatmap) library(readxl) library(janitor) library(rms) library(DT)
#Demographics Table
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data_subset <- circ_data %>%
select(
Age,
Gender,
ECOG,
pT,
pN,
LVI,
PNI,
Grade,
Location,
Surg.Type,
Stage,
ACT,
BRAF.V600E,
RAS,
MSI,
RFS.Event,
OS.Event,
OS.months) %>%
mutate(
Age = as.numeric(Age),
Gender = factor(Gender, levels = c("Male", "Female")),
ECOG = factor(ECOG, levels = c(0, 1)),
pT = factor(pT, levels = c("T1-T2", "T3-T4")),
pN = factor(pN, levels = c("N0", "N1-N2")),
LVI = factor(LVI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No")),
PNI = factor(PNI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No")),
Grade = factor(Grade, levels = c("G1", "G2/G3")),
Location = factor(Location, levels = c("Upper rectum (Ra)", "Lower rectum (Rb)")),
Surg.Type = factor(Surg.Type),
Stage = factor(Stage, levels = c("II", "III")),
ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF wt", "BRAF V600E")),
RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS wt", "RAS mut")),
MSI = factor(MSI, levels = c("MSS", "MSI-High")),
RFS.Event = factor(RFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
table1| Characteristic | N = 2501 |
|---|---|
| Age | 67 (38 - 92) |
| Gender | |
| Â Â Â Â Male | 162 (65%) |
| Â Â Â Â Female | 88 (35%) |
| ECOG | |
| Â Â Â Â 0 | 228 (91%) |
| Â Â Â Â 1 | 22 (8.8%) |
| pT | |
| Â Â Â Â T1-T2 | 29 (12%) |
| Â Â Â Â T3-T4 | 221 (88%) |
| pN | |
| Â Â Â Â N0 | 99 (40%) |
| Â Â Â Â N1-N2 | 151 (60%) |
| LVI | 220 (88%) |
| PNI | 109 (44%) |
| Grade | |
| Â Â Â Â G1 | 144 (58%) |
| Â Â Â Â G2/G3 | 106 (42%) |
| Location | |
| Â Â Â Â Upper rectum (Ra) | 127 (51%) |
| Â Â Â Â Lower rectum (Rb) | 123 (49%) |
| Surg.Type | |
| Â Â Â Â APR | 35 (14%) |
| Â Â Â Â HAR | 8 (3.2%) |
| Â Â Â Â Hartmann procedure | 2 (0.8%) |
| Â Â Â Â ISR | 11 (4.4%) |
| Â Â Â Â LAR | 189 (76%) |
| Â Â Â Â Other | 4 (1.6%) |
| Â Â Â Â TPE | 1 (0.4%) |
| Stage | |
| Â Â Â Â II | 100 (40%) |
| Â Â Â Â III | 150 (60%) |
| ACT | |
| Â Â Â Â Adjuvant Chemotherapy | 123 (49%) |
| Â Â Â Â Observation | 127 (51%) |
| BRAF.V600E | |
| Â Â Â Â BRAF wt | 248 (99%) |
| Â Â Â Â BRAF V600E | 2 (0.8%) |
| RAS | |
| Â Â Â Â RAS wt | 122 (49%) |
| Â Â Â Â RAS mut | 128 (51%) |
| MSI | |
| Â Â Â Â MSS | 247 (99%) |
| Â Â Â Â MSI-High | 3 (1.2%) |
| RFS.Event | |
| Â Â Â Â Recurrence | 48 (19%) |
| Â Â Â Â No Recurrence | 202 (81%) |
| OS.Event | |
| Â Â Â Â Deceased | 11 (4.4%) |
| Â Â Â Â Alive | 239 (96%) |
| OS.months | 22.0 (1.2 - 35.9) |
| 1 Median (Min - Max); n (%) | |
fit1 <- as_flex_table(
table1,
include = everything(),
return_calls = FALSE)
fit1Characteristic | N = 2501 |
|---|---|
Age | 67 (38 - 92) |
Gender | |
Male | 162 (65%) |
Female | 88 (35%) |
ECOG | |
0 | 228 (91%) |
1 | 22 (8.8%) |
pT | |
T1-T2 | 29 (12%) |
T3-T4 | 221 (88%) |
pN | |
N0 | 99 (40%) |
N1-N2 | 151 (60%) |
LVI | 220 (88%) |
PNI | 109 (44%) |
Grade | |
G1 | 144 (58%) |
G2/G3 | 106 (42%) |
Location | |
Upper rectum (Ra) | 127 (51%) |
Lower rectum (Rb) | 123 (49%) |
Surg.Type | |
APR | 35 (14%) |
HAR | 8 (3.2%) |
Hartmann procedure | 2 (0.8%) |
ISR | 11 (4.4%) |
LAR | 189 (76%) |
Other | 4 (1.6%) |
TPE | 1 (0.4%) |
Stage | |
II | 100 (40%) |
III | 150 (60%) |
ACT | |
Adjuvant Chemotherapy | 123 (49%) |
Observation | 127 (51%) |
BRAF.V600E | |
BRAF wt | 248 (99%) |
BRAF V600E | 2 (0.8%) |
RAS | |
RAS wt | 122 (49%) |
RAS mut | 128 (51%) |
MSI | |
MSS | 247 (99%) |
MSI-High | 3 (1.2%) |
RFS.Event | |
Recurrence | 48 (19%) |
No Recurrence | 202 (81%) |
OS.Event | |
Deceased | 11 (4.4%) |
Alive | 239 (96%) |
OS.months | 22.0 (1.2 - 35.9) |
1Median (Min - Max); n (%) | |
save_as_docx(fit1, path= "~/Downloads/table1.docx")#Demographics Table by MRD ctDNA Status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data_subset1 <- circ_data %>%
select(
Age,
Gender,
ECOG,
pT,
pN,
LVI,
PNI,
Grade,
Location,
Surg.Type,
Stage,
ACT,
BRAF.V600E,
RAS,
MSI,
RFS.Event,
OS.Event,
OS.months) %>%
mutate(
Age = as.numeric(Age),
Gender = factor(Gender, levels = c("Male", "Female")),
ECOG = factor(ECOG, levels = c(0, 1)),
pT = factor(pT, levels = c("T1-T2", "T3-T4")),
pN = factor(pN, levels = c("N0", "N1-N2")),
LVI = factor(LVI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No")),
PNI = factor(PNI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No")),
Grade = factor(Grade, levels = c("G1", "G2/G3")),
Location = factor(Location, levels = c("Upper rectum (Ra)", "Lower rectum (Rb)")),
Surg.Type = factor(Surg.Type),
Stage = factor(Stage, levels = c("II", "III")),
ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF wt", "BRAF V600E")),
RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS wt", "RAS mut")),
MSI = factor(MSI, levels = c("MSS", "MSI-High")),
RFS.Event = factor(RFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
OS.months = as.numeric(OS.months))
circ_data1 <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data_subset2 <- circ_data %>%
select(
Age,
Gender,
ECOG,
pT,
pN,
LVI,
PNI,
Grade,
Location,
Surg.Type,
Stage,
ACT,
BRAF.V600E,
RAS,
MSI,
RFS.Event,
OS.Event,
OS.months,
ctDNA.MRD) %>%
mutate(
Age = as.numeric(Age),
Gender = factor(Gender, levels = c("Male", "Female")),
ECOG = factor(ECOG, levels = c(0, 1)),
pT = factor(pT, levels = c("T1-T2", "T3-T4")),
pN = factor(pN, levels = c("N0", "N1-N2")),
LVI = factor(LVI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No")),
PNI = factor(PNI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No")),
Grade = factor(Grade, levels = c("G1", "G2/G3")),
Location = factor(Location, levels = c("Upper rectum (Ra)", "Lower rectum (Rb)")),
Surg.Type = factor(Surg.Type),
Stage = factor(Stage, levels = c("II", "III")),
ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF wt", "BRAF V600E")),
RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS wt", "RAS mut")),
MSI = factor(MSI, levels = c("MSS", "MSI-High")),
RFS.Event = factor(RFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
OS.months = as.numeric(OS.months),
ctDNA.MRD = factor(ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE")))
Overall <- circ_data_subset1 %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
Overall| Characteristic | N = 2471 |
|---|---|
| Age | 67 (38 - 92) |
| Gender | |
| Â Â Â Â Male | 162 (66%) |
| Â Â Â Â Female | 85 (34%) |
| ECOG | |
| Â Â Â Â 0 | 225 (91%) |
| Â Â Â Â 1 | 22 (8.9%) |
| pT | |
| Â Â Â Â T1-T2 | 29 (12%) |
| Â Â Â Â T3-T4 | 218 (88%) |
| pN | |
| Â Â Â Â N0 | 98 (40%) |
| Â Â Â Â N1-N2 | 149 (60%) |
| LVI | 218 (88%) |
| PNI | 107 (43%) |
| Grade | |
| Â Â Â Â G1 | 142 (57%) |
| Â Â Â Â G2/G3 | 105 (43%) |
| Location | |
| Â Â Â Â Upper rectum (Ra) | 126 (51%) |
| Â Â Â Â Lower rectum (Rb) | 121 (49%) |
| Surg.Type | |
| Â Â Â Â APR | 35 (14%) |
| Â Â Â Â HAR | 8 (3.2%) |
| Â Â Â Â Hartmann procedure | 2 (0.8%) |
| Â Â Â Â ISR | 11 (4.5%) |
| Â Â Â Â LAR | 186 (75%) |
| Â Â Â Â Other | 4 (1.6%) |
| Â Â Â Â TPE | 1 (0.4%) |
| Stage | |
| Â Â Â Â II | 99 (40%) |
| Â Â Â Â III | 148 (60%) |
| ACT | |
| Â Â Â Â Adjuvant Chemotherapy | 121 (49%) |
| Â Â Â Â Observation | 126 (51%) |
| BRAF.V600E | |
| Â Â Â Â BRAF wt | 245 (99%) |
| Â Â Â Â BRAF V600E | 2 (0.8%) |
| RAS | |
| Â Â Â Â RAS wt | 121 (49%) |
| Â Â Â Â RAS mut | 126 (51%) |
| MSI | |
| Â Â Â Â MSS | 244 (99%) |
| Â Â Â Â MSI-High | 3 (1.2%) |
| RFS.Event | |
| Â Â Â Â Recurrence | 48 (19%) |
| Â Â Â Â No Recurrence | 199 (81%) |
| OS.Event | |
| Â Â Â Â Deceased | 11 (4.5%) |
| Â Â Â Â Alive | 236 (96%) |
| OS.months | 22.0 (1.2 - 35.9) |
| 1 Median (Min - Max); n (%) | |
ByctDNA_MRD <- circ_data_subset2 %>%
tbl_summary(
by = ctDNA.MRD, # add this line to subgroup by ctDNA.MRD
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
add_p() %>%
bold_labels()
ByctDNA_MRD| Characteristic | NEGATIVE N = 2121 |
POSITIVE N = 351 |
p-value2 |
|---|---|---|---|
| Age | 67 (38 - 92) | 67 (43 - 82) | 0.6 |
| Gender | 0.5 | ||
| Â Â Â Â Male | 141 (67%) | 21 (60%) | |
| Â Â Â Â Female | 71 (33%) | 14 (40%) | |
| ECOG | 0.3 | ||
| Â Â Â Â 0 | 191 (90%) | 34 (97%) | |
| Â Â Â Â 1 | 21 (9.9%) | 1 (2.9%) | |
| pT | 0.8 | ||
| Â Â Â Â T1-T2 | 26 (12%) | 3 (8.6%) | |
| Â Â Â Â T3-T4 | 186 (88%) | 32 (91%) | |
| pN | <0.001 | ||
| Â Â Â Â N0 | 95 (45%) | 3 (8.6%) | |
| Â Â Â Â N1-N2 | 117 (55%) | 32 (91%) | |
| LVI | 184 (87%) | 34 (97%) | 0.092 |
| PNI | 86 (41%) | 21 (60%) | 0.032 |
| Grade | 0.7 | ||
| Â Â Â Â G1 | 123 (58%) | 19 (54%) | |
| Â Â Â Â G2/G3 | 89 (42%) | 16 (46%) | |
| Location | 0.5 | ||
| Â Â Â Â Upper rectum (Ra) | 110 (52%) | 16 (46%) | |
| Â Â Â Â Lower rectum (Rb) | 102 (48%) | 19 (54%) | |
| Surg.Type | 0.5 | ||
| Â Â Â Â APR | 27 (13%) | 8 (23%) | |
| Â Â Â Â HAR | 7 (3.3%) | 1 (2.9%) | |
| Â Â Â Â Hartmann procedure | 2 (0.9%) | 0 (0%) | |
| Â Â Â Â ISR | 11 (5.2%) | 0 (0%) | |
| Â Â Â Â LAR | 161 (76%) | 25 (71%) | |
| Â Â Â Â Other | 3 (1.4%) | 1 (2.9%) | |
| Â Â Â Â TPE | 1 (0.5%) | 0 (0%) | |
| Stage | <0.001 | ||
| Â Â Â Â II | 96 (45%) | 3 (8.6%) | |
| Â Â Â Â III | 116 (55%) | 32 (91%) | |
| ACT | 0.004 | ||
| Â Â Â Â Adjuvant Chemotherapy | 96 (45%) | 25 (71%) | |
| Â Â Â Â Observation | 116 (55%) | 10 (29%) | |
| BRAF.V600E | >0.9 | ||
| Â Â Â Â BRAF wt | 210 (99%) | 35 (100%) | |
| Â Â Â Â BRAF V600E | 2 (0.9%) | 0 (0%) | |
| RAS | 0.060 | ||
| Â Â Â Â RAS wt | 109 (51%) | 12 (34%) | |
| Â Â Â Â RAS mut | 103 (49%) | 23 (66%) | |
| MSI | >0.9 | ||
| Â Â Â Â MSS | 209 (99%) | 35 (100%) | |
| Â Â Â Â MSI-High | 3 (1.4%) | 0 (0%) | |
| RFS.Event | <0.001 | ||
| Â Â Â Â Recurrence | 23 (11%) | 25 (71%) | |
| Â Â Â Â No Recurrence | 189 (89%) | 10 (29%) | |
| OS.Event | 0.2 | ||
| Â Â Â Â Deceased | 8 (3.8%) | 3 (8.6%) | |
| Â Â Â Â Alive | 204 (96%) | 32 (91%) | |
| OS.months | 22.0 (7.5 - 35.9) | 16.4 (1.2 - 34.9) | 0.082 |
| 1 Median (Min - Max); n (%) | |||
| 2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test | |||
merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table| Characteristic |
Table 1
|
Table 2
|
||
|---|---|---|---|---|
| N = 2471 | NEGATIVE N = 2121 |
POSITIVE N = 351 |
p-value2 | |
| Age | 67 (38 - 92) | 67 (38 - 92) | 67 (43 - 82) | 0.6 |
| Gender | 0.5 | |||
| Â Â Â Â Male | 162 (66%) | 141 (67%) | 21 (60%) | |
| Â Â Â Â Female | 85 (34%) | 71 (33%) | 14 (40%) | |
| ECOG | 0.3 | |||
| Â Â Â Â 0 | 225 (91%) | 191 (90%) | 34 (97%) | |
| Â Â Â Â 1 | 22 (8.9%) | 21 (9.9%) | 1 (2.9%) | |
| pT | 0.8 | |||
| Â Â Â Â T1-T2 | 29 (12%) | 26 (12%) | 3 (8.6%) | |
| Â Â Â Â T3-T4 | 218 (88%) | 186 (88%) | 32 (91%) | |
| pN | <0.001 | |||
| Â Â Â Â N0 | 98 (40%) | 95 (45%) | 3 (8.6%) | |
| Â Â Â Â N1-N2 | 149 (60%) | 117 (55%) | 32 (91%) | |
| LVI | 218 (88%) | 184 (87%) | 34 (97%) | 0.092 |
| PNI | 107 (43%) | 86 (41%) | 21 (60%) | 0.032 |
| Grade | 0.7 | |||
| Â Â Â Â G1 | 142 (57%) | 123 (58%) | 19 (54%) | |
| Â Â Â Â G2/G3 | 105 (43%) | 89 (42%) | 16 (46%) | |
| Location | 0.5 | |||
| Â Â Â Â Upper rectum (Ra) | 126 (51%) | 110 (52%) | 16 (46%) | |
| Â Â Â Â Lower rectum (Rb) | 121 (49%) | 102 (48%) | 19 (54%) | |
| Surg.Type | 0.5 | |||
| Â Â Â Â APR | 35 (14%) | 27 (13%) | 8 (23%) | |
| Â Â Â Â HAR | 8 (3.2%) | 7 (3.3%) | 1 (2.9%) | |
| Â Â Â Â Hartmann procedure | 2 (0.8%) | 2 (0.9%) | 0 (0%) | |
| Â Â Â Â ISR | 11 (4.5%) | 11 (5.2%) | 0 (0%) | |
| Â Â Â Â LAR | 186 (75%) | 161 (76%) | 25 (71%) | |
| Â Â Â Â Other | 4 (1.6%) | 3 (1.4%) | 1 (2.9%) | |
| Â Â Â Â TPE | 1 (0.4%) | 1 (0.5%) | 0 (0%) | |
| Stage | <0.001 | |||
| Â Â Â Â II | 99 (40%) | 96 (45%) | 3 (8.6%) | |
| Â Â Â Â III | 148 (60%) | 116 (55%) | 32 (91%) | |
| ACT | 0.004 | |||
| Â Â Â Â Adjuvant Chemotherapy | 121 (49%) | 96 (45%) | 25 (71%) | |
| Â Â Â Â Observation | 126 (51%) | 116 (55%) | 10 (29%) | |
| BRAF.V600E | >0.9 | |||
| Â Â Â Â BRAF wt | 245 (99%) | 210 (99%) | 35 (100%) | |
| Â Â Â Â BRAF V600E | 2 (0.8%) | 2 (0.9%) | 0 (0%) | |
| RAS | 0.060 | |||
| Â Â Â Â RAS wt | 121 (49%) | 109 (51%) | 12 (34%) | |
| Â Â Â Â RAS mut | 126 (51%) | 103 (49%) | 23 (66%) | |
| MSI | >0.9 | |||
| Â Â Â Â MSS | 244 (99%) | 209 (99%) | 35 (100%) | |
| Â Â Â Â MSI-High | 3 (1.2%) | 3 (1.4%) | 0 (0%) | |
| RFS.Event | <0.001 | |||
| Â Â Â Â Recurrence | 48 (19%) | 23 (11%) | 25 (71%) | |
| Â Â Â Â No Recurrence | 199 (81%) | 189 (89%) | 10 (29%) | |
| OS.Event | 0.2 | |||
| Â Â Â Â Deceased | 11 (4.5%) | 8 (3.8%) | 3 (8.6%) | |
| Â Â Â Â Alive | 236 (96%) | 204 (96%) | 32 (91%) | |
| OS.months | 22.0 (1.2 - 35.9) | 22.0 (7.5 - 35.9) | 16.4 (1.2 - 34.9) | 0.082 |
| 1 Median (Min - Max); n (%) | ||||
| 2 Wilcoxon rank sum test; Pearson’s Chi-squared test; Fisher’s exact test | ||||
fit1 <- as_flex_table(
merged_table,
include = everything(),
return_calls = FALSE)
fit1
| Table 1 | Table 2 | ||
|---|---|---|---|---|
Characteristic | N = 2471 | NEGATIVE | POSITIVE | p-value2 |
Age | 67 (38 - 92) | 67 (38 - 92) | 67 (43 - 82) | 0.6 |
Gender | 0.5 | |||
Male | 162 (66%) | 141 (67%) | 21 (60%) | |
Female | 85 (34%) | 71 (33%) | 14 (40%) | |
ECOG | 0.3 | |||
0 | 225 (91%) | 191 (90%) | 34 (97%) | |
1 | 22 (8.9%) | 21 (9.9%) | 1 (2.9%) | |
pT | 0.8 | |||
T1-T2 | 29 (12%) | 26 (12%) | 3 (8.6%) | |
T3-T4 | 218 (88%) | 186 (88%) | 32 (91%) | |
pN | <0.001 | |||
N0 | 98 (40%) | 95 (45%) | 3 (8.6%) | |
N1-N2 | 149 (60%) | 117 (55%) | 32 (91%) | |
LVI | 218 (88%) | 184 (87%) | 34 (97%) | 0.092 |
PNI | 107 (43%) | 86 (41%) | 21 (60%) | 0.032 |
Grade | 0.7 | |||
G1 | 142 (57%) | 123 (58%) | 19 (54%) | |
G2/G3 | 105 (43%) | 89 (42%) | 16 (46%) | |
Location | 0.5 | |||
Upper rectum (Ra) | 126 (51%) | 110 (52%) | 16 (46%) | |
Lower rectum (Rb) | 121 (49%) | 102 (48%) | 19 (54%) | |
Surg.Type | 0.5 | |||
APR | 35 (14%) | 27 (13%) | 8 (23%) | |
HAR | 8 (3.2%) | 7 (3.3%) | 1 (2.9%) | |
Hartmann procedure | 2 (0.8%) | 2 (0.9%) | 0 (0%) | |
ISR | 11 (4.5%) | 11 (5.2%) | 0 (0%) | |
LAR | 186 (75%) | 161 (76%) | 25 (71%) | |
Other | 4 (1.6%) | 3 (1.4%) | 1 (2.9%) | |
TPE | 1 (0.4%) | 1 (0.5%) | 0 (0%) | |
Stage | <0.001 | |||
II | 99 (40%) | 96 (45%) | 3 (8.6%) | |
III | 148 (60%) | 116 (55%) | 32 (91%) | |
ACT | 0.004 | |||
Adjuvant Chemotherapy | 121 (49%) | 96 (45%) | 25 (71%) | |
Observation | 126 (51%) | 116 (55%) | 10 (29%) | |
BRAF.V600E | >0.9 | |||
BRAF wt | 245 (99%) | 210 (99%) | 35 (100%) | |
BRAF V600E | 2 (0.8%) | 2 (0.9%) | 0 (0%) | |
RAS | 0.060 | |||
RAS wt | 121 (49%) | 109 (51%) | 12 (34%) | |
RAS mut | 126 (51%) | 103 (49%) | 23 (66%) | |
MSI | >0.9 | |||
MSS | 244 (99%) | 209 (99%) | 35 (100%) | |
MSI-High | 3 (1.2%) | 3 (1.4%) | 0 (0%) | |
RFS.Event | <0.001 | |||
Recurrence | 48 (19%) | 23 (11%) | 25 (71%) | |
No Recurrence | 199 (81%) | 189 (89%) | 10 (29%) | |
OS.Event | 0.2 | |||
Deceased | 11 (4.5%) | 8 (3.8%) | 3 (8.6%) | |
Alive | 236 (96%) | 204 (96%) | 32 (91%) | |
OS.months | 22.0 (1.2 - 35.9) | 22.0 (7.5 - 35.9) | 16.4 (1.2 - 34.9) | 0.082 |
1Median (Min - Max); n (%) | ||||
2Wilcoxon rank sum test; Pearson's Chi-squared test; Fisher's exact test | ||||
save_as_docx(fit1, path= "~/Downloads/merged_table.docx")#DFS in Complete Cohort (N=250)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~CohortB, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
CohortB, data = circ_data)
n events median 0.95LCL 0.95UCL
[1,] 250 53 NA NA NAsurv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ CohortB, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="DFS - Complete Cohort (n=250)", ylab= "Progression-Free Survival", xlab="Months from Surgery", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))Call: survfit(formula = surv_object ~ CohortB, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 163 42 0.826 0.0245 0.772 0.868
24 37 10 0.766 0.0294 0.702 0.818#OS in Complete Cohort (N=250)
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~CohortB, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
CohortB, data = circ_data)
n events median 0.95LCL 0.95UCL
[1,] 250 11 NA NA NAsurv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ CohortB, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="OS - Complete Cohort (n=250)", ylab= "Overall Survival", xlab="Months from Surgery", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))Call: survfit(formula = surv_object ~ CohortB, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 195 3 0.988 0.00691 0.963 0.996
24 55 7 0.934 0.02271 0.872 0.966#ctDNA Detection Rates by Window and Stages
#ctDNA at Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data$ctDNA.Baseline <- factor(circ_data$ctDNA.Baseline, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Baseline %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II", "III"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Baseline == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Baseline, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Baseline == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#ctDNA at MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II", "III"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#ctDNA at Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Surveillance %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II", "III"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)#ctDNA MRD Detection rate Stage II vs III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"))
circ_data$Stage_Grouped <- factor(ifelse(circ_data$Stage %in% c("II", "II"), "II", "III"))
contingency_table <- table(circ_data$Stage_Grouped, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(contingency_table)
NEGATIVE POSITIVE
II 96 3
III 116 32print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 15.364, df = 1, p-value = 8.865e-05#ctDNA Surveillance Detection rate Stage II vs III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"))
circ_data$Stage_Grouped <- factor(ifelse(circ_data$Stage %in% c("II", "II"), "II", "III"))
contingency_table <- table(circ_data$Stage_Grouped, circ_data$ctDNA.Surveillance)
chi_square_test <- chisq.test(contingency_table)
print(contingency_table)
NEGATIVE POSITIVE
II 86 8
III 107 32print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 7.3146, df = 1, p-value = 0.00684#ctDNA timepoints cadence at the MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data$MRD_Time_weeks <- circ_data$ctDNA.MRD.Time / 7
# Plot the histogram
hist(circ_data$MRD_Time_weeks,
col = 'gray',
main = 'Surgery to first ctDNA testing at MRD window',
xlab = 'Weeks from surgery to first ctDNA test at MRD window',
ylab = 'ctDNA Samples',
ylim = c(0, 120),
xlim = c(0, 10),
breaks = seq(0, 24, 1),
xaxp = c(0, 24, 24))
#DFS by ctDNA at the MRD Window - All stages Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 211 28 NA NA NA
ctDNA.MRD=POSITIVE 35 25 9.27 7.62 14.1event_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | All stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 18, 24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 146 21 0.895 0.0218 0.843 0.930
18 97 4 0.868 0.0250 0.810 0.910
24 31 2 0.850 0.0275 0.787 0.896
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 8 22 0.350 0.0830 0.1948 0.509
18 5 3 0.219 0.0792 0.0884 0.386
24 2 0 0.219 0.0792 0.0884 0.386circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 246, number of events= 53
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.2985 9.9592 0.2795 8.222 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 9.959 0.1004 5.758 17.23
Concordance= 0.709 (se = 0.032 )
Likelihood ratio test= 55.38 on 1 df, p=1e-13
Wald test = 67.61 on 1 df, p=<2e-16
Score (logrank) test = 101.2 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 9.96 (5.76-17.23); p = 0"#Fisher test for DFS percentages at 12, 18, and 24 months
dfs_times <- c(12, 18, 24)
p_values <- sapply(dfs_times, function(time) {
neg_count <- sum(circ_data$ctDNA.MRD == "NEGATIVE" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
pos_count <- sum(circ_data$ctDNA.MRD == "POSITIVE" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
neg_total <- sum(circ_data$ctDNA.MRD == "NEGATIVE")
pos_total <- sum(circ_data$ctDNA.MRD == "POSITIVE")
neg_surv <- neg_total - sum(circ_data$ctDNA.MRD == "NEGATIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
pos_surv <- pos_total - sum(circ_data$ctDNA.MRD == "POSITIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
test_result <- fisher.test(surv_matrix)
return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)p-value at 12 months p-value at 18 months p-value at 24 months
3.289740e-11 6.618049e-13 2.261958e-12 #Multivariate cox regression at MRD Window for DFS - All stages Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", ">70"))
circ_data$ECOG <- factor(circ_data$ECOG, levels = c("0", "1"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$LVI <- factor(circ_data$LVI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No"))
circ_data$PNI <- factor(circ_data$PNI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No"))
circ_data$Grade <- factor(circ_data$Grade, levels = c("G1", "G2/G3"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("Wild-Type", "Mutant"))
surv_object <- Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.MRD + Gender + Age.Group + ECOG + pT + pN + LVI + PNI + Grade + RAS, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for DFS - All Stages", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#DFS by ctDNA at the MRD Window - Stage II Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("III")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 96 6 NA NA NA
ctDNA.MRD=POSITIVE 3 3 13 0.493 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 73 3 0.967 0.0189 0.900 0.989
24 13 3 0.923 0.0307 0.835 0.965
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.0000 2.0000 1.0000 0.6667 0.2722 0.0541 0.9452 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 99, number of events= 9
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 3.3011 27.1423 0.7369 4.48 7.46e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 27.14 0.03684 6.404 115
Concordance= 0.66 (se = 0.077 )
Likelihood ratio test= 12.56 on 1 df, p=4e-04
Wald test = 20.07 on 1 df, p=7e-06
Score (logrank) test = 45.99 on 1 df, p=1e-11cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 27.14 (6.4-115.04); p = 0"#Fisher test for DFS percentages at 12 and 24 months
dfs_times <- c(12, 24)
p_values <- sapply(dfs_times, function(time) {
neg_count <- sum(circ_data$ctDNA.MRD == "NEGATIVE" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
pos_count <- sum(circ_data$ctDNA.MRD == "POSITIVE" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
neg_total <- sum(circ_data$ctDNA.MRD == "NEGATIVE")
pos_total <- sum(circ_data$ctDNA.MRD == "POSITIVE")
neg_surv <- neg_total - sum(circ_data$ctDNA.MRD == "NEGATIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
pos_surv <- pos_total - sum(circ_data$ctDNA.MRD == "POSITIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
test_result <- fisher.test(surv_matrix)
return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)p-value at 12 months p-value at 24 months
0.1175270483 0.0005355469 #DFS by ctDNA at the MRD Window - Stage III Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("II")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
1 observation deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 1189 203 NA NA NA
ctDNA.MRD=POSITIVE 291 233 5.06 4.6 6.51event_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
1 observation deleted due to missingness
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 391 195 0.806 0.0130 0.779 0.830
30 228 6 0.791 0.0141 0.762 0.817
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 30 229 0.189 0.0245 0.144 0.239
30 17 3 0.166 0.0250 0.120 0.218circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 1480, number of events= 436
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.24488 9.43924 0.09775 22.97 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 9.439 0.1059 7.794 11.43
Concordance= 0.729 (se = 0.011 )
Likelihood ratio test= 467.9 on 1 df, p=<2e-16
Wald test = 527.5 on 1 df, p=<2e-16
Score (logrank) test = 768.2 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 9.44 (7.79-11.43); p = 0"#Fisher test for DFS percentages at 24 and 30 months
dfs_times <- c(24, 30)
circ_data <- na.omit(circ_data[, c("ctDNA.MRD", "DFS.MRD.months", "DFS.Event")])
p_values <- sapply(dfs_times, function(time) {
neg_count <- sum(circ_data$ctDNA.MRD == "NEGATIVE" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
pos_count <- sum(circ_data$ctDNA.MRD == "POSITIVE" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
neg_total <- sum(circ_data$ctDNA.MRD == "NEGATIVE")
pos_total <- sum(circ_data$ctDNA.MRD == "POSITIVE")
neg_surv <- neg_total - sum(circ_data$ctDNA.MRD == "NEGATIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
pos_surv <- pos_total - sum(circ_data$ctDNA.MRD == "POSITIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
test_result <- fisher.test(surv_matrix)
return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)p-value at 24 months p-value at 30 months
6.506858e-91 5.794183e-92 #DFS by ACT treatment in MRD negative - Stage II/III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 115 16 NA NA NA
ACT=TRUE 96 12 NA NA NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | Stage II/III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 16.0000 16.0000 0.8419 0.0373 0.7521 0.9013
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 15.0000 11.0000 0.8612 0.0407 0.7574 0.9228 circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 211, number of events= 28
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.06529 1.06747 0.38220 0.171 0.864
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 1.067 0.9368 0.5047 2.258
Concordance= 0.517 (se = 0.048 )
Likelihood ratio test= 0.03 on 1 df, p=0.9
Wald test = 0.03 on 1 df, p=0.9
Score (logrank) test = 0.03 on 1 df, p=0.9cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.07 (0.5-2.26); p = 0.864"#Fisher test for DFS percentages at 24
dfs_times <- c(24)
p_values <- sapply(dfs_times, function(time) {
neg_count <- sum(circ_data$ACT == "TRUE" & circ_data$DFS.months >= time & circ_data$DFS.Event == 0)
pos_count <- sum(circ_data$ACT == "FALSE" & circ_data$DFS.months >= time & circ_data$DFS.Event == 0)
neg_total <- sum(circ_data$ACT == "TRUE")
pos_total <- sum(circ_data$ACT == "FALSE")
neg_surv <- neg_total - sum(circ_data$ACT == "TRUE" & circ_data$DFS.Event == 1 & circ_data$DFS.months < time)
pos_surv <- pos_total - sum(circ_data$ACT == "FALSE" & circ_data$DFS.Event == 1 & circ_data$DFS.months < time)
surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
test_result <- fisher.test(surv_matrix)
return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)p-value at 24 months
0.6811626 #Adjusted HR "ACT vs no ACT" - Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + Stage + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + Stage +
ECOG, data = circ_data)
n= 211, number of events= 28
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -0.5269 0.5904 0.4211 -1.251 0.21080
GenderMale 0.1145 1.1213 0.4081 0.280 0.77910
Age.Group≥70 0.3280 1.3883 0.4022 0.816 0.41472
StageIII 1.5211 4.5774 0.4886 3.113 0.00185 **
ECOG1 0.5215 1.6846 0.5509 0.947 0.34383
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.5904 1.6937 0.2587 1.348
GenderMale 1.1213 0.8918 0.5039 2.495
Age.Group≥70 1.3883 0.7203 0.6311 3.054
StageIII 4.5774 0.2185 1.7569 11.926
ECOG1 1.6846 0.5936 0.5722 4.960
Concordance= 0.71 (se = 0.043 )
Likelihood ratio test= 12.82 on 5 df, p=0.03
Wald test = 11.73 on 5 df, p=0.04
Score (logrank) test = 12.73 on 5 df, p=0.03#DFS by ACT treatment in MRD positive - Stage II/III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 6 5 5.62 1.22 NA
ACT=TRUE 25 16 9.33 6.97 NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | Stage II/III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(6, 24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6.000 2.000 3.000 0.500 0.204 0.111 0.804
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6 21 4 0.840 0.0733 0.628 0.937
24 2 12 0.295 0.1026 0.118 0.497circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 31, number of events= 21
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.3036 3.6825 0.5572 2.339 0.0193 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 3.682 0.2716 1.235 10.98
Concordance= 0.593 (se = 0.045 )
Likelihood ratio test= 4.59 on 1 df, p=0.03
Wald test = 5.47 on 1 df, p=0.02
Score (logrank) test = 6.25 on 1 df, p=0.01cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 3.68 (1.24-10.98); p = 0.019"#Adjusted HR "ACT vs no ACT" - Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + Stage + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + Stage +
ECOG, data = circ_data)
n= 31, number of events= 21
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -1.27835 0.27850 0.59142 -2.162 0.0307 *
GenderMale 0.35495 1.42611 0.51244 0.693 0.4885
Age.Group≥70 0.12911 1.13782 0.45951 0.281 0.7787
StageIII 0.08804 1.09203 0.81274 0.108 0.9137
ECOG1 1.40597 4.07950 1.13540 1.238 0.2156
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.2785 3.5907 0.08738 0.8876
GenderMale 1.4261 0.7012 0.52236 3.8935
Age.Group≥70 1.1378 0.8789 0.46231 2.8003
StageIII 1.0920 0.9157 0.22204 5.3708
ECOG1 4.0795 0.2451 0.44071 37.7622
Concordance= 0.632 (se = 0.066 )
Likelihood ratio test= 6.01 on 5 df, p=0.3
Wald test = 6.81 on 5 df, p=0.2
Score (logrank) test = 7.76 on 5 df, p=0.2#DFS by ctDNA at 3 months - All stages Landmark 3 months timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.3months!="",]
circ_data <- circ_data[circ_data$DFS.3mo.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event)~ctDNA.3months, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event) ~
ctDNA.3months, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.3months=NEGATIVE 204 31 NA NA NA
ctDNA.3months=POSITIVE 19 14 8.28 5.59 NAevent_summary <- circ_data %>%
group_by(ctDNA.3months) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.3months, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA 3 months | All stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.3months, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.3months=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 137 26 0.863 0.0252 0.805 0.905
24 30 4 0.829 0.0293 0.762 0.879
ctDNA.3months=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 4 14 0.237 0.101 0.0758 0.447
24 3 0 0.237 0.101 0.0758 0.447circ_data$ctDNA.3months <- factor(circ_data$ctDNA.3months, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.3months, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.3months, data = circ_data)
n= 223, number of events= 45
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.3monthsPOSITIVE 2.0768 7.9792 0.3247 6.397 1.58e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.3monthsPOSITIVE 7.979 0.1253 4.223 15.08
Concordance= 0.641 (se = 0.034 )
Likelihood ratio test= 29.34 on 1 df, p=6e-08
Wald test = 40.92 on 1 df, p=2e-10
Score (logrank) test = 57.62 on 1 df, p=3e-14cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 7.98 (4.22-15.08); p = 0"#Fisher test for DFS percentages at 12 and 24 months
dfs_times <- c(12, 24)
p_values <- sapply(dfs_times, function(time) {
neg_count <- sum(circ_data$ctDNA.3months == "NEGATIVE" & circ_data$DFS.3mo.months >= time & circ_data$DFS.Event == 0)
pos_count <- sum(circ_data$ctDNA.3months == "POSITIVE" & circ_data$DFS.3mo.months >= time & circ_data$DFS.Event == 0)
neg_total <- sum(circ_data$ctDNA.3months == "NEGATIVE")
pos_total <- sum(circ_data$ctDNA.3months == "POSITIVE")
neg_surv <- neg_total - sum(circ_data$ctDNA.3months == "NEGATIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.3mo.months < time)
pos_surv <- pos_total - sum(circ_data$ctDNA.3months == "POSITIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.3mo.months < time)
surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
test_result <- fisher.test(surv_matrix)
return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)p-value at 12 months p-value at 24 months
2.546518e-08 1.138180e-07 #DFS by ctDNA at 6 months - All stages Landmark 6 months timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.6months!="",]
circ_data <- circ_data[circ_data$DFS.6mo.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)~ctDNA.6months, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event) ~
ctDNA.6months, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.6months=NEGATIVE 173 21 NA NA NA
ctDNA.6months=POSITIVE 9 7 3.68 2.6 NAevent_summary <- circ_data %>%
group_by(ctDNA.6months) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6months, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA 6 months | All stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(6, 24))Call: survfit(formula = surv_object ~ ctDNA.6months, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.6months=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6 134 14 0.915 0.0217 0.861 0.949
24 14 7 0.832 0.0420 0.730 0.898
ctDNA.6months=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6.0000 2.0000 6.0000 0.3333 0.1571 0.0783 0.6226 circ_data$ctDNA.6months <- factor(circ_data$ctDNA.6months, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.6months, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.6months, data = circ_data)
n= 182, number of events= 28
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.6monthsPOSITIVE 2.7187 15.1598 0.4577 5.94 2.85e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.6monthsPOSITIVE 15.16 0.06596 6.182 37.18
Concordance= 0.628 (se = 0.042 )
Likelihood ratio test= 22.48 on 1 df, p=2e-06
Wald test = 35.29 on 1 df, p=3e-09
Score (logrank) test = 62.3 on 1 df, p=3e-15cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 15.16 (6.18-37.18); p = 0"#Fisher test for DFS percentages at 6 and 24 months
dfs_times <- c(6, 24)
p_values <- sapply(dfs_times, function(time) {
neg_count <- sum(circ_data$ctDNA.6months == "NEGATIVE" & circ_data$DFS.6mo.months >= time & circ_data$DFS.Event == 0)
pos_count <- sum(circ_data$ctDNA.6months == "POSITIVE" & circ_data$DFS.6mo.months >= time & circ_data$DFS.Event == 0)
neg_total <- sum(circ_data$ctDNA.6months == "NEGATIVE")
pos_total <- sum(circ_data$ctDNA.6months == "POSITIVE")
neg_surv <- neg_total - sum(circ_data$ctDNA.6months == "NEGATIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.6mo.months < time)
pos_surv <- pos_total - sum(circ_data$ctDNA.6months == "POSITIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.6mo.months < time)
surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
test_result <- fisher.test(surv_matrix)
return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values) p-value at 6 months p-value at 24 months
5.667547e-05 2.922826e-05 #DFS by ctDNA Clearance ACT-treated at 3 months - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "POSITIVE" & ctDNA.3months == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.3months == "POSITIVE" ~ 2
))
circ_data <- circ_data[circ_data$DFS.3mo.months>=0,]
survfit(Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
97 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 16 8 14.16 6.01 NA
ctDNA.Dynamics=2 8 7 8.05 5.59 NAevent_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Clearance from MRD to 3 months ACT-treated | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Clearance", "No Clearance"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
97 observations deleted due to missingness
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 5 7 0.516 0.139 0.229 0.742
24 2 1 0.413 0.145 0.146 0.665
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.00000 1.00000 7.00000 0.12500 0.11693 0.00659 0.42271 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2"), labels = c("Clearance", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 24, number of events= 15
(97 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsNo Clearance 0.8707 2.3885 0.5227 1.666 0.0958 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsNo Clearance 2.389 0.4187 0.8574 6.654
Concordance= 0.605 (se = 0.066 )
Likelihood ratio test= 2.65 on 1 df, p=0.1
Wald test = 2.77 on 1 df, p=0.1
Score (logrank) test = 2.95 on 1 df, p=0.09cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.39 (0.86-6.65); p = 0.096"#Fisher test for DFS percentages at 12 and 24 months
dfs_times <- c(12)
circ_data <- na.omit(circ_data[, c("ctDNA.Dynamics", "DFS.3mo.months", "DFS.Event")])
p_values <- sapply(dfs_times, function(time) {
neg_count <- sum(circ_data$ctDNA.Dynamics == "Clearance" & circ_data$DFS.3mo.months >= time & circ_data$DFS.Event == FALSE)
pos_count <- sum(circ_data$ctDNA.Dynamics == "No Clearance" & circ_data$DFS.3mo.months >= time & circ_data$DFS.Event == FALSE)
neg_total <- sum(circ_data$ctDNA.Dynamics == "Clearance")
pos_total <- sum(circ_data$ctDNA.Dynamics == "No Clearance")
neg_surv <- neg_total - sum(circ_data$ctDNA.Dynamics == "Clearance" & circ_data$DFS.Event == TRUE & circ_data$DFS.3mo.months < time)
pos_surv <- pos_total - sum(circ_data$ctDNA.Dynamics == "No Clearance" & circ_data$DFS.Event == TRUE & circ_data$DFS.3mo.months < time)
surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
test_result <- fisher.test(surv_matrix)
return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)p-value at 12 months
0.07907586 #DFS by ctDNA Clearance ACT-treated at 6 months - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "POSITIVE" & ctDNA.6months == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.6months == "POSITIVE" ~ 2
))
circ_data <- circ_data[circ_data$DFS.6mo.months>=0,]
survfit(Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
106 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 13 5 NA 5.52 NA
ctDNA.Dynamics=2 2 2 2.68 2.60 NAevent_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Clearance from MRD to 6 months ACT-treated | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Clearance", "No Clearance"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
106 observations deleted due to missingness
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 4 5 0.542 0.162 0.204 0.789
24 2 0 0.542 0.162 0.204 0.789
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CIcirc_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2"), labels = c("Clearance", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 15, number of events= 7
(106 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsNo Clearance 3.017 20.438 1.237 2.44 0.0147 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsNo Clearance 20.44 0.04893 1.81 230.8
Concordance= 0.669 (se = 0.087 )
Likelihood ratio test= 5.94 on 1 df, p=0.01
Wald test = 5.95 on 1 df, p=0.01
Score (logrank) test = 11.68 on 1 df, p=6e-04cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 20.44 (1.81-230.77); p = 0.015"#Percentages of MRD negative with molecular recurrence (returned positive) post-MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "" & circ_data$Lead.Time >= 0, ]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$PostMRDPos.Event=="TRUE",]
circ_datadf <- as.data.frame(circ_data)
# Convert days to months
circ_data$PostMRDPos.months <- circ_data$PostMRDPos/30.437
# Define the intervals: 0-6, 6-9, 9-12, 12-15, 15-18, 18-21, 21-24, >24 months
breaks <- c(0, 6, 9, 12, 15, 18, 21, 24, 48)
labels <- c("0-6m", "6-9m", "9-12m", "12-15m", "15-18m", "18-21m", "21-24m", ">24m")
# Categorize p_drelReturned_months into intervals
circ_data$p_drelReturned_intervals <- cut(circ_data$PostMRDPos.months, breaks = breaks, labels = labels, right = FALSE)
# Examine the distribution of the intervals
table(circ_data$p_drelReturned_intervals)
0-6m 6-9m 9-12m 12-15m 15-18m 18-21m 21-24m >24m
9 5 7 1 4 0 0 0 # Get the counts for each interval
interval_counts <- table(circ_data$p_drelReturned_intervals)
# Calculate the percentages
interval_percentages <- 100 * interval_counts / sum(interval_counts)
# Combine the counts and percentages for a clearer overview
interval_summary <- data.frame(Counts = interval_counts, Percentages = interval_percentages)
# Calculate the total number of observations
total_observations <- sum(interval_counts)
# Add the total number of observations to the summary
interval_summary$TotalObservations <- c(rep(NA, length(interval_counts)-1), total_observations)
# Print the summary with total observations
print(interval_summary)
# Calculate cumulative percentages
cumulative_percentages <- cumsum(interval_percentages)
# Combine the counts, percentages, and cumulative percentages for a clearer overview
interval_summary <- data.frame(Counts = interval_counts, Percentages = interval_percentages, CumulativePercentages = cumulative_percentages, TotalObservations = c(rep(NA, length(interval_counts)-1), total_observations))
bp <- barplot(interval_percentages,
main="Molecular Recurrence post-MRD in MRD negative patients",
xlab="Months Interval from Surgery",
ylab="Patients % converted positive",
col="lightblue",
ylim=c(0, 100))
# Add the cumulative percentages to the plot
points(bp, cumulative_percentages, type="o", pch=22, col="red", cex=1.5)
print(interval_summary)#DFS by ctDNA MRD positive vs ctDNA negative with molecular recurrence at Surveillance - 3 groups
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="NEGATIVE" ~ 1,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="POSITIVE" ~ 2,
ctDNA.MRD == "POSITIVE" ~ 3
))
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
7 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 180 10 NA NA NA
ctDNA.Dynamics=2 24 14 13.44 10.55 NA
ctDNA.Dynamics=3 35 25 9.27 7.62 14.1event_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","red"), title="DFS - ctDNA MRD Pos vs Neg with Molecular Recurrence at Surveillance Window", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("All-time negative","Molecular Recurrence", "ctDNA MRD Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
7 observations deleted due to missingness
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 131 7 0.959 0.0151 0.916 0.98
24 28 2 0.942 0.0194 0.889 0.97
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 12 10 0.569 0.104 0.3457 0.742
24 2 4 0.285 0.122 0.0863 0.525
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 8 22 0.350 0.0830 0.1948 0.509
24 2 3 0.219 0.0792 0.0884 0.386circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3"), labels = c("All-time negative","Molecular Recurrence", "ctDNA MRD Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 239, number of events= 49
(7 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsMolecular Recurrence 2.6086 13.5800 0.4162 6.267 3.68e-10 ***
ctDNA.DynamicsctDNA MRD Positive 3.2402 25.5396 0.3786 8.559 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsMolecular Recurrence 13.58 0.07364 6.006 30.70
ctDNA.DynamicsctDNA MRD Positive 25.54 0.03915 12.162 53.63
Concordance= 0.834 (se = 0.029 )
Likelihood ratio test= 94.98 on 2 df, p=<2e-16
Wald test = 74.17 on 2 df, p=<2e-16
Score (logrank) test = 146.1 on 2 df, p=<2e-16#Fisher test for DFS percentages at 12 and 24 months - All time negative vs Molecular Recurrence
dfs_times <- c(12, 24)
circ_data <- na.omit(circ_data[, c("ctDNA.Dynamics", "DFS.MRD.months", "DFS.Event")])
p_values <- sapply(dfs_times, function(time) {
neg_count <- sum(circ_data$ctDNA.Dynamics == "All-time negative" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
pos_count <- sum(circ_data$ctDNA.Dynamics == "Molecular Recurrence" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
neg_total <- sum(circ_data$ctDNA.Dynamics == "All-time negative")
pos_total <- sum(circ_data$ctDNA.Dynamics == "Molecular Recurrence")
neg_surv <- neg_total - sum(circ_data$ctDNA.Dynamics == "All-time negative" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
pos_surv <- pos_total - sum(circ_data$ctDNA.Dynamics == "Molecular Recurrence" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
test_result <- fisher.test(surv_matrix)
return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)p-value at 12 months p-value at 24 months
8.575063e-07 6.424306e-10 #Repeat analysis to run Fisher test for All time negative vs ctDNA Positive
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="NEGATIVE" ~ 1,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="POSITIVE" ~ 2,
ctDNA.MRD == "POSITIVE" ~ 3
))
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
7 observations deleted due to missingness
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 131 7 0.959 0.0151 0.916 0.98
24 28 2 0.942 0.0194 0.889 0.97
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 12 10 0.569 0.104 0.3457 0.742
24 2 4 0.285 0.122 0.0863 0.525
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 8 22 0.350 0.0830 0.1948 0.509
24 2 3 0.219 0.0792 0.0884 0.386circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3"), labels = c("All-time negative","Molecular Recurrence", "ctDNA MRD Positive"))
#Fisher test for DFS percentages at 12 and 24 months - All time negative vs ctDNA Positive
dfs_times <- c(12, 24)
circ_data <- na.omit(circ_data[, c("ctDNA.Dynamics", "DFS.MRD.months", "DFS.Event")])
p_values <- sapply(dfs_times, function(time) {
neg_count <- sum(circ_data$ctDNA.Dynamics == "All-time negative" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
pos_count <- sum(circ_data$ctDNA.Dynamics == "ctDNA MRD Positive" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
neg_total <- sum(circ_data$ctDNA.Dynamics == "All-time negative")
pos_total <- sum(circ_data$ctDNA.Dynamics == "ctDNA MRD Positive")
neg_surv <- neg_total - sum(circ_data$ctDNA.Dynamics == "All-time negative" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
pos_surv <- pos_total - sum(circ_data$ctDNA.Dynamics == "ctDNA MRD Positive" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
test_result <- fisher.test(surv_matrix)
return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)p-value at 12 months p-value at 24 months
2.382798e-15 1.933852e-17 #Repeat analysis to run Fisher test for Molecular Recurrence vs ctDNA Positive
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="NEGATIVE" ~ 1,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="POSITIVE" ~ 2,
ctDNA.MRD == "POSITIVE" ~ 3
))
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
7 observations deleted due to missingness
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 131 7 0.959 0.0151 0.916 0.98
24 28 2 0.942 0.0194 0.889 0.97
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 12 10 0.569 0.104 0.3457 0.742
24 2 4 0.285 0.122 0.0863 0.525
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 8 22 0.350 0.0830 0.1948 0.509
24 2 3 0.219 0.0792 0.0884 0.386circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3"), labels = c("All-time negative","Molecular Recurrence", "ctDNA MRD Positive"))
#Fisher test for DFS percentages at 12 and 24 months - Molecular Recurrence vs ctDNA Positive
dfs_times <- c(12, 24)
circ_data <- na.omit(circ_data[, c("ctDNA.Dynamics", "DFS.MRD.months", "DFS.Event")])
p_values <- sapply(dfs_times, function(time) {
neg_count <- sum(circ_data$ctDNA.Dynamics == "Molecular Recurrence" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
pos_count <- sum(circ_data$ctDNA.Dynamics == "ctDNA MRD Positive" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
neg_total <- sum(circ_data$ctDNA.Dynamics == "Molecular Recurrence")
pos_total <- sum(circ_data$ctDNA.Dynamics == "ctDNA MRD Positive")
neg_surv <- neg_total - sum(circ_data$ctDNA.Dynamics == "Molecular Recurrence" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
pos_surv <- pos_total - sum(circ_data$ctDNA.Dynamics == "ctDNA MRD Positive" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
test_result <- fisher.test(surv_matrix)
return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)p-value at 12 months p-value at 24 months
0.1214065 0.4023373 #Repeat to compare HRs for Molecular Recurrence vs ctDNA MRD positive
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="NEGATIVE" ~ 1,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="POSITIVE" ~ 2,
ctDNA.MRD == "POSITIVE" ~ 3
))
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("2","3"), labels = c("Molecular Recurrence", "ctDNA MRD Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 59, number of events= 39
(187 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsctDNA MRD Positive 0.6281 1.8741 0.3361 1.869 0.0616 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsctDNA MRD Positive 1.874 0.5336 0.9699 3.621
Concordance= 0.608 (se = 0.037 )
Likelihood ratio test= 3.65 on 1 df, p=0.06
Wald test = 3.49 on 1 df, p=0.06
Score (logrank) test = 3.6 on 1 df, p=0.06cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.87 (0.97-3.62); p = 0.062"#DFS by ctDNA at the Surveillance Window - All stages Landmark 10 weeks
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 193 10 NA NA NA
ctDNA.Surveillance=POSITIVE 40 30 8.79 7.16 12event_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | All stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 138 8 0.955 0.0156 0.912 0.977
24 31 1 0.946 0.0181 0.896 0.972
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 12 26 0.336 0.0765 0.1940 0.485
24 2 4 0.154 0.0733 0.0461 0.321circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 233, number of events= 40
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 3.2170 24.9530 0.3726 8.633 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 24.95 0.04008 12.02 51.8
Concordance= 0.822 (se = 0.033 )
Likelihood ratio test= 88.32 on 1 df, p=<2e-16
Wald test = 74.53 on 1 df, p=<2e-16
Score (logrank) test = 156.3 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 24.95 (12.02-51.8); p = 0"#Fisher test for DFS percentages at 12 and 24 months
dfs_times <- c(12, 24)
circ_data <- na.omit(circ_data[, c("ctDNA.Surveillance", "DFS.months", "DFS.Event")])
p_values <- sapply(dfs_times, function(time) {
neg_count <- sum(circ_data$ctDNA.Surveillance == "NEGATIVE" & circ_data$DFS.months >= time & circ_data$DFS.Event == 0)
pos_count <- sum(circ_data$ctDNA.Surveillance == "POSITIVE" & circ_data$DFS.months >= time & circ_data$DFS.Event == 0)
neg_total <- sum(circ_data$ctDNA.Surveillance == "NEGATIVE")
pos_total <- sum(circ_data$ctDNA.Surveillance == "POSITIVE")
neg_surv <- neg_total - sum(circ_data$ctDNA.Surveillance == "NEGATIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.months < time)
pos_surv <- pos_total - sum(circ_data$ctDNA.Surveillance == "POSITIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.months < time)
surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
test_result <- fisher.test(surv_matrix)
return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)p-value at 12 months p-value at 24 months
1.175567e-17 2.041854e-21 #Multivariate cox regression at Surveillance Window for DFS - All stages Landmark 10 weeks
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", ">70"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Left-sided colon", "Right-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels = c("0", "1"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$LVI <- factor(circ_data$LVI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No"))
circ_data$PNI <- factor(circ_data$PNI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No"))
circ_data$Grade <- factor(circ_data$Grade, levels = c("G1", "G2/G3"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("Wild-Type", "Mutant"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance + Gender + Age.Group + ECOG + pT + pN + LVI + PNI + Grade + RAS, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for DFS - All Stages", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)#DFS by ctDNA Dynamics from MRD to 6 months - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.6mo.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.6months == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.6months == "NEGATIVE" ~ 2,
ctDNA.MRD == "NEGATIVE" & ctDNA.6months == "POSITIVE" ~ 3,
ctDNA.MRD == "POSITIVE" & ctDNA.6months == "POSITIVE" ~ 4
))
survfit(Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
66 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 157 16 NA NA NA
ctDNA.Dynamics=2 14 5 NA 5.52 NA
ctDNA.Dynamics=3 4 2 8.51 0.00 NA
ctDNA.Dynamics=4 5 5 2.76 2.60 NAevent_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to 6 months | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
66 observations deleted due to missingness
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 12.000 16.000 0.851 0.045 0.736 0.919
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 2.000 5.000 0.542 0.162 0.204 0.789
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
ctDNA.Dynamics=4
time n.risk n.event survival std.err lower 95% CI upper 95% CIcirc_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3", "4"), labels = c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 180, number of events= 28
(66 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsConverted Negative 1.5689 4.8013 0.5147 3.048 0.00230 **
ctDNA.DynamicsConverted Positive 2.1062 8.2170 0.7582 2.778 0.00547 **
ctDNA.DynamicsPersistently Positive 3.8173 45.4828 0.5900 6.470 9.8e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsConverted Negative 4.801 0.20828 1.751 13.17
ctDNA.DynamicsConverted Positive 8.217 0.12170 1.859 36.32
ctDNA.DynamicsPersistently Positive 45.483 0.02199 14.310 144.57
Concordance= 0.697 (se = 0.048 )
Likelihood ratio test= 33.4 on 3 df, p=3e-07
Wald test = 46.4 on 3 df, p=5e-10
Score (logrank) test = 106.2 on 3 df, p=<2e-16#Table with recurrence sites by ctDNA at the MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data_subset1 <- circ_data %>%
select(
Rel.Site) %>%
mutate(
Rel.Site = factor(Rel.Site, levels = c("Local", "Lymph Node", "Liver", "Lung", "Peritoneum", "Brain")))
circ_data_subset2 <- circ_data %>%
select(
Rel.Site,
ctDNA.MRD) %>%
mutate(
Rel.Site = factor(Rel.Site, levels = c("Local", "Lymph Node", "Liver", "Lung", "Peritoneum", "Brain")),
ctDNA.MRD = factor(ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE")))
Overall <- circ_data_subset1 %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
Overall| Characteristic | N = 481 |
|---|---|
| Rel.Site | |
| Â Â Â Â Local | 4 (8.3%) |
| Â Â Â Â Lymph Node | 5 (10%) |
| Â Â Â Â Liver | 17 (35%) |
| Â Â Â Â Lung | 18 (38%) |
| Â Â Â Â Peritoneum | 3 (6.3%) |
| Â Â Â Â Brain | 1 (2.1%) |
| 1 n (%) | |
ByctDNA_MRD <- circ_data_subset2 %>%
tbl_summary(
by = ctDNA.MRD, # add this line to subgroup by ctDNA.MRD
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
add_p() %>%
bold_labels()
ByctDNA_MRD| Characteristic | NEGATIVE N = 231 |
POSITIVE N = 251 |
p-value2 |
|---|---|---|---|
| Rel.Site | <0.001 | ||
| Â Â Â Â Local | 2 (8.7%) | 2 (8.0%) | |
| Â Â Â Â Lymph Node | 1 (4.3%) | 4 (16%) | |
| Â Â Â Â Liver | 2 (8.7%) | 15 (60%) | |
| Â Â Â Â Lung | 15 (65%) | 3 (12%) | |
| Â Â Â Â Peritoneum | 2 (8.7%) | 1 (4.0%) | |
| Â Â Â Â Brain | 1 (4.3%) | 0 (0%) | |
| 1 n (%) | |||
| 2 Fisher’s exact test | |||
merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table| Characteristic |
Table 1
|
Table 2
|
||
|---|---|---|---|---|
| N = 481 | NEGATIVE N = 231 |
POSITIVE N = 251 |
p-value2 | |
| Rel.Site | <0.001 | |||
| Â Â Â Â Local | 4 (8.3%) | 2 (8.7%) | 2 (8.0%) | |
| Â Â Â Â Lymph Node | 5 (10%) | 1 (4.3%) | 4 (16%) | |
| Â Â Â Â Liver | 17 (35%) | 2 (8.7%) | 15 (60%) | |
| Â Â Â Â Lung | 18 (38%) | 15 (65%) | 3 (12%) | |
| Â Â Â Â Peritoneum | 3 (6.3%) | 2 (8.7%) | 1 (4.0%) | |
| Â Â Â Â Brain | 1 (2.1%) | 1 (4.3%) | 0 (0%) | |
| 1 n (%) | ||||
| 2 Fisher’s exact test | ||||
fit1 <- as_flex_table(
merged_table,
include = everything(),
return_calls = FALSE)
fit1
| Table 1 | Table 2 | ||
|---|---|---|---|---|
Characteristic | N = 481 | NEGATIVE | POSITIVE | p-value2 |
Rel.Site | <0.001 | |||
Local | 4 (8.3%) | 2 (8.7%) | 2 (8.0%) | |
Lymph Node | 5 (10%) | 1 (4.3%) | 4 (16%) | |
Liver | 17 (35%) | 2 (8.7%) | 15 (60%) | |
Lung | 18 (38%) | 15 (65%) | 3 (12%) | |
Peritoneum | 3 (6.3%) | 2 (8.7%) | 1 (4.0%) | |
Brain | 1 (2.1%) | 1 (4.3%) | 0 (0%) | |
1n (%) | ||||
2Fisher's exact test | ||||
save_as_docx(fit1, path= "~/Downloads/merged_table.docx")#MTM/mL levels at the MRD Window by Radiological Recurrence
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_data <- as.data.frame(circ_data)
# Transform p_MRD_MTM with log10
circ_data$p_MRD_MTM <- as.numeric(as.character(circ_data$p_MRD_MTM))
circ_data$RFS.Event <- factor(circ_data$RFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
summary_stats <- circ_data %>%
group_by(RFS.Event) %>%
summarise(
median_p_MRD_MTM = median(p_MRD_MTM, na.rm = TRUE),
range_p_MRD_MTM = paste0(min(p_MRD_MTM, na.rm = TRUE), " - ", max(p_MRD_MTM, na.rm = TRUE))
)
print(summary_stats)
m3_1v2 <- wilcox.test(p_MRD_MTM ~ RFS.Event,
data = circ_data[circ_data$RFS.Event %in% c("No Recurrence", "Recurrence"), ],
na.rm = TRUE)
print(m3_1v2)
Wilcoxon rank sum test with continuity correction
data: p_MRD_MTM by RFS.Event
W = 2458, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0boxplot(p_MRD_MTM~RFS.Event, data=circ_data, main="ctDNA MRD Window MTM - Recurrence", xlab="Recurrence", ylab="MTM/mL", col="white",border="black", ylim = c(0, 50))
#MTM/mL levels at the MRD Window by Radiological Recurrence Sites
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_data <- as.data.frame(circ_data)
# Transform p_MRD_MTM with log10
circ_data$p_MRD_MTM <- as.numeric(as.character(circ_data$p_MRD_MTM))
circ_data$Rel.Site <- factor(circ_data$Rel.Site, levels = c("Liver", "Lymph Node", "Local", "Peritoneum", "Lung", "Brain"))
summary_stats <- circ_data %>%
group_by(Rel.Site) %>%
summarise(
median_p_MRD_MTM = median(p_MRD_MTM, na.rm = TRUE),
range_p_MRD_MTM = paste0(min(p_MRD_MTM, na.rm = TRUE), " - ", max(p_MRD_MTM, na.rm = TRUE))
)
print(summary_stats)kruskal_test <- kruskal.test(p_MRD_MTM ~ Rel.Site, data = circ_data)
print(kruskal_test)
Kruskal-Wallis rank sum test
data: p_MRD_MTM by Rel.Site
Kruskal-Wallis chi-squared = 20.366, df = 5, p-value = 0.001067boxplot(p_MRD_MTM~Rel.Site, data=circ_data, main="ctDNA MRD Window MTM - Recurrence Site", xlab="Recurrence Site", ylab="MTM/mL", col="white",border="black", ylim = c(0, 40))
#Barplot with Recurrence Sites (Liver vs Others) by ctDNA at the MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Rel.Site <- factor(circ_data$Rel.Site, levels = c("Liver", "Lymph Node", "Local", "Peritoneum", "Lung", "Brain"))
contingency_table <- table(circ_data$Rel.Site, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)Warning: Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test
data: contingency_table
X-squared = 21.028, df = 5, p-value = 0.0008004fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 5.02e-05
alternative hypothesis: two.sidedprint(contingency_table)
Negative Positive
Liver 2 15
Lymph Node 1 4
Local 2 2
Peritoneum 2 1
Lung 15 3
Brain 1 0table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "Patients with Radiological Recurrence",
x = "Recurrence Site",
y = "Patients (%)",
fill = "ctDNA at MRD",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Negative" = "blue", "Positive" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Detection ctDNA rates based on sites of relapse
# Remove existing objects and set the working directory
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
# Create a table of counts for the "Rel.Site" variable
relsite_counts <- table(circ_data$Rel.Site)
relsite_df <- as.data.frame(relsite_counts)
names(relsite_df) <- c("Rel.Site", "Count")
circ_data_pos_mrd <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data_pos_anytime <- circ_data[circ_data$ctDNA.anytime=="POSITIVE",]
pos_counts_mrd <- table(circ_data_pos_mrd$Rel.Site)
pos_counts_anytime <- table(circ_data_pos_anytime$Rel.Site)
relsite_df$MRDPos_Count <- ifelse(is.na(match(relsite_df$Rel.Site, names(pos_counts_mrd))), 0, pos_counts_mrd[match(relsite_df$Rel.Site, names(pos_counts_mrd))])
relsite_df$MRDPos_Count[is.na(relsite_df$MRDPos_Count)] <- 0
relsite_df$AnytimePos_Count <- ifelse(is.na(match(relsite_df$Rel.Site, names(pos_counts_anytime))), 0, pos_counts_anytime[match(relsite_df$Rel.Site, names(pos_counts_anytime))])
relsite_df$AnytimePos_Count[is.na(relsite_df$AnytimePos_Count)] <- 0
relsite_df$Percent <- (relsite_df$Count / sum(relsite_df$Count)) * 100
relsite_df$MRDPos_Percent <- (relsite_df$MRDPos_Count / relsite_df$Count) * 100
relsite_df$AnytimePos_Percent <- (relsite_df$AnytimePos_Count / relsite_df$Count) * 100
total_observations <- sum(relsite_df$Count)
total_pos_mrd <- sum(relsite_df$MRDPos_Count)
total_pos_anytime <- sum(relsite_df$AnytimePos_Count)
total_row <- data.frame(Rel.Site = "Total", Count = total_observations, MRDPos_Count = total_pos_mrd, AnytimePos_Count = total_pos_anytime, Percent = 100, MRDPos_Percent = (total_pos_mrd / total_observations) * 100, AnytimePos_Percent = (total_pos_anytime / total_observations) * 100)
relsite_df <- rbind(relsite_df, total_row)
print(relsite_df)
ft <- flextable(relsite_df)
doc <- read_docx() %>%
body_add_flextable(value = ft)
print(doc, target = "relsite_df.docx")---
title: "CIRCULATE Galaxy cohort B Ando et al 2024"
output: html_notebook
---

library(swimplot)
library(coxphf)
library(grid)
library(gtable)
library(readr) 
library(mosaic)
library(dplyr)
library(officer)
library(flextable)
library(survival) 
library(survminer)
library(gridtext)
library(ggplot2)
library(scales)
library(ggthemes)
library(tidyverse)
library(gtsummary)
library(flextable)
library(parameters)
library(car)
library(grid)
library(ComplexHeatmap)
library(readxl)
library(janitor)
library(rms)
library(DT)

#Demographics Table
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]

circ_data_subset <- circ_data %>%
  select(
    Age,
    Gender,
    ECOG,
    pT,
    pN,
    LVI,
    PNI,
    Grade,
    Location,
    Surg.Type,
    Stage,
    ACT,
    BRAF.V600E,
    RAS,
    MSI,
    RFS.Event,
    OS.Event,
    OS.months) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    ECOG = factor(ECOG, levels = c(0, 1)),
    pT = factor(pT, levels = c("T1-T2", "T3-T4")),
    pN = factor(pN, levels = c("N0", "N1-N2")),
    LVI = factor(LVI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No")),
    PNI = factor(PNI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No")),
    Grade = factor(Grade, levels = c("G1", "G2/G3")),
    Location = factor(Location, levels = c("Upper rectum (Ra)", "Lower rectum (Rb)")),
    Surg.Type = factor(Surg.Type),
    Stage = factor(Stage, levels = c("II", "III")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF wt", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS wt", "RAS mut")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")),
    RFS.Event = factor(RFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
    OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
    OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE)
fit1
save_as_docx(fit1, path= "~/Downloads/table1.docx")
```


#Demographics Table by MRD ctDNA Status
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]

circ_data_subset1 <- circ_data %>%
  select(
    Age,
    Gender,
    ECOG,
    pT,
    pN,
    LVI,
    PNI,
    Grade,
    Location,
    Surg.Type,
    Stage,
    ACT,
    BRAF.V600E,
    RAS,
    MSI,
    RFS.Event,
    OS.Event,
    OS.months) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    ECOG = factor(ECOG, levels = c(0, 1)),
    pT = factor(pT, levels = c("T1-T2", "T3-T4")),
    pN = factor(pN, levels = c("N0", "N1-N2")),
    LVI = factor(LVI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No")),
    PNI = factor(PNI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No")),
    Grade = factor(Grade, levels = c("G1", "G2/G3")),
    Location = factor(Location, levels = c("Upper rectum (Ra)", "Lower rectum (Rb)")),
    Surg.Type = factor(Surg.Type),
    Stage = factor(Stage, levels = c("II", "III")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF wt", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS wt", "RAS mut")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")),
    RFS.Event = factor(RFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
    OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
    OS.months = as.numeric(OS.months))

circ_data1 <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]

circ_data_subset2 <- circ_data %>%
  select(
    Age,
    Gender,
    ECOG,
    pT,
    pN,
    LVI,
    PNI,
    Grade,
    Location,
    Surg.Type,
    Stage,
    ACT,
    BRAF.V600E,
    RAS,
    MSI,
    RFS.Event,
    OS.Event,
    OS.months,
    ctDNA.MRD) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    ECOG = factor(ECOG, levels = c(0, 1)),
    pT = factor(pT, levels = c("T1-T2", "T3-T4")),
    pN = factor(pN, levels = c("N0", "N1-N2")),
    LVI = factor(LVI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No")),
    PNI = factor(PNI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No")),
    Grade = factor(Grade, levels = c("G1", "G2/G3")),
    Location = factor(Location, levels = c("Upper rectum (Ra)", "Lower rectum (Rb)")),
    Surg.Type = factor(Surg.Type),
    Stage = factor(Stage, levels = c("II", "III")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF wt", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS wt", "RAS mut")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")),
    RFS.Event = factor(RFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
    OS.Event = factor(OS.Event, levels = c("TRUE", "FALSE"), labels = c("Deceased", "Alive")),
    OS.months = as.numeric(OS.months),
    ctDNA.MRD = factor(ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE")))
Overall <- circ_data_subset1 %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
Overall

ByctDNA_MRD <- circ_data_subset2 %>%
  tbl_summary(
    by = ctDNA.MRD, # add this line to subgroup by ctDNA.MRD
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels()
ByctDNA_MRD

merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table

fit1 <- as_flex_table(
  merged_table,
  include = everything(),
  return_calls = FALSE)
fit1
save_as_docx(fit1, path= "~/Downloads/merged_table.docx")
```

#DFS in Complete Cohort (N=250)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~CohortB, data = circ_data)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ CohortB, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="DFS - Complete Cohort (n=250)", ylab= "Progression-Free Survival", xlab="Months from Surgery", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
```

#OS in Complete Cohort (N=250)
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~CohortB, data = circ_data)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ CohortB, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue"), title="OS - Complete Cohort (n=250)", ylab= "Overall Survival", xlab="Months from Surgery", legend.labs=c("Complete cohort"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
```

#ctDNA Detection Rates by Window and Stages
```{r}
#ctDNA at Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data$ctDNA.Baseline <- factor(circ_data$ctDNA.Baseline, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Baseline %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II", "III"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Baseline == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Baseline, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Baseline == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA at MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II", "III"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA at Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Surveillance %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("II", "III"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
```



#ctDNA MRD Detection rate Stage II vs III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"))
circ_data$Stage_Grouped <- factor(ifelse(circ_data$Stage %in% c("II", "II"), "II", "III"))
contingency_table <- table(circ_data$Stage_Grouped, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(contingency_table)
print(chi_square_test)
```

#ctDNA Surveillance Detection rate Stage II vs III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"))
circ_data$Stage_Grouped <- factor(ifelse(circ_data$Stage %in% c("II", "II"), "II", "III"))
contingency_table <- table(circ_data$Stage_Grouped, circ_data$ctDNA.Surveillance)
chi_square_test <- chisq.test(contingency_table)
print(contingency_table)
print(chi_square_test)
```

#ctDNA timepoints cadence at the MRD Window
```{r}
rm(list=ls())
setwd("~/Downloads") 
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]

circ_data$MRD_Time_weeks <- circ_data$ctDNA.MRD.Time / 7

# Plot the histogram
hist(circ_data$MRD_Time_weeks,
     col = 'gray',
     main = 'Surgery to first ctDNA testing at MRD window',
     xlab = 'Weeks from surgery to first ctDNA test at MRD window',
     ylab = 'ctDNA Samples',
     ylim = c(0, 120),
     xlim = c(0, 10),
     breaks = seq(0, 24, 1),
     xaxp = c(0, 24, 24))
```

#DFS by ctDNA at the MRD Window - All stages Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | All stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 18, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Fisher test for DFS percentages at 12, 18, and 24 months
dfs_times <- c(12, 18, 24)
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$ctDNA.MRD == "NEGATIVE" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
  pos_count <- sum(circ_data$ctDNA.MRD == "POSITIVE" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
  neg_total <- sum(circ_data$ctDNA.MRD == "NEGATIVE")
  pos_total <- sum(circ_data$ctDNA.MRD == "POSITIVE")
  
  neg_surv <- neg_total - sum(circ_data$ctDNA.MRD == "NEGATIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
  pos_surv <- pos_total - sum(circ_data$ctDNA.MRD == "POSITIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
```




#Multivariate cox regression at MRD Window for DFS - All stages Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", ">70"))
circ_data$ECOG <- factor(circ_data$ECOG, levels = c("0", "1"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$LVI <- factor(circ_data$LVI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No"))
circ_data$PNI <- factor(circ_data$PNI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No"))
circ_data$Grade <- factor(circ_data$Grade, levels = c("G1", "G2/G3"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("Wild-Type", "Mutant"))
surv_object <- Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.MRD + Gender + Age.Group + ECOG + pT + pN + LVI + PNI + Grade + RAS, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for DFS - All Stages", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```


#DFS by ctDNA at the MRD Window - Stage II Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("III")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Fisher test for DFS percentages at 12 and 24 months
dfs_times <- c(12, 24)
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$ctDNA.MRD == "NEGATIVE" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
  pos_count <- sum(circ_data$ctDNA.MRD == "POSITIVE" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
  neg_total <- sum(circ_data$ctDNA.MRD == "NEGATIVE")
  pos_total <- sum(circ_data$ctDNA.MRD == "POSITIVE")
  
  neg_surv <- neg_total - sum(circ_data$ctDNA.MRD == "NEGATIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
  pos_surv <- pos_total - sum(circ_data$ctDNA.MRD == "POSITIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
```

#DFS by ctDNA at the MRD Window - Stage III Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("II")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Fisher test for DFS percentages at 24 and 30 months
dfs_times <- c(24, 30)
circ_data <- na.omit(circ_data[, c("ctDNA.MRD", "DFS.MRD.months", "DFS.Event")])
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$ctDNA.MRD == "NEGATIVE" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
  pos_count <- sum(circ_data$ctDNA.MRD == "POSITIVE" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
  neg_total <- sum(circ_data$ctDNA.MRD == "NEGATIVE")
  pos_total <- sum(circ_data$ctDNA.MRD == "POSITIVE")
  
  neg_surv <- neg_total - sum(circ_data$ctDNA.MRD == "NEGATIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
  pos_surv <- pos_total - sum(circ_data$ctDNA.MRD == "POSITIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
```






#DFS by ACT treatment in MRD negative - Stage II/III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | Stage II/III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Fisher test for DFS percentages at 24
dfs_times <- c(24)
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$ACT == "TRUE" & circ_data$DFS.months >= time & circ_data$DFS.Event == 0)
  pos_count <- sum(circ_data$ACT == "FALSE" & circ_data$DFS.months >= time & circ_data$DFS.Event == 0)
  neg_total <- sum(circ_data$ACT == "TRUE")
  pos_total <- sum(circ_data$ACT == "FALSE")
  
  neg_surv <- neg_total - sum(circ_data$ACT == "TRUE" & circ_data$DFS.Event == 1 & circ_data$DFS.months < time)
  pos_surv <- pos_total - sum(circ_data$ACT == "FALSE" & circ_data$DFS.Event == 1 & circ_data$DFS.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)

#Adjusted HR "ACT vs no ACT" - Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + Stage + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ACT treatment in MRD positive - Stage II/III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | Stage II/III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(6, 24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Adjusted HR "ACT vs no ACT" - Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + Stage + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ctDNA at 3 months - All stages Landmark 3 months timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.3months!="",]
circ_data <- circ_data[circ_data$DFS.3mo.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event)~ctDNA.3months, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.3months) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.3months, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA 3 months | All stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.3months <- factor(circ_data$ctDNA.3months, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.3months, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Fisher test for DFS percentages at 12 and 24 months
dfs_times <- c(12, 24)
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$ctDNA.3months == "NEGATIVE" & circ_data$DFS.3mo.months >= time & circ_data$DFS.Event == 0)
  pos_count <- sum(circ_data$ctDNA.3months == "POSITIVE" & circ_data$DFS.3mo.months >= time & circ_data$DFS.Event == 0)
  neg_total <- sum(circ_data$ctDNA.3months == "NEGATIVE")
  pos_total <- sum(circ_data$ctDNA.3months == "POSITIVE")
  
  neg_surv <- neg_total - sum(circ_data$ctDNA.3months == "NEGATIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.3mo.months < time)
  pos_surv <- pos_total - sum(circ_data$ctDNA.3months == "POSITIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.3mo.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
```




#DFS by ctDNA at 6 months - All stages Landmark 6 months timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.6months!="",]
circ_data <- circ_data[circ_data$DFS.6mo.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)~ctDNA.6months, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.6months) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.6months, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA 6 months | All stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(6, 24))
circ_data$ctDNA.6months <- factor(circ_data$ctDNA.6months, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.6months, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Fisher test for DFS percentages at 6 and 24 months
dfs_times <- c(6, 24)
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$ctDNA.6months == "NEGATIVE" & circ_data$DFS.6mo.months >= time & circ_data$DFS.Event == 0)
  pos_count <- sum(circ_data$ctDNA.6months == "POSITIVE" & circ_data$DFS.6mo.months >= time & circ_data$DFS.Event == 0)
  neg_total <- sum(circ_data$ctDNA.6months == "NEGATIVE")
  pos_total <- sum(circ_data$ctDNA.6months == "POSITIVE")
  
  neg_surv <- neg_total - sum(circ_data$ctDNA.6months == "NEGATIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.6mo.months < time)
  pos_surv <- pos_total - sum(circ_data$ctDNA.6months == "POSITIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.6mo.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
```




#DFS by ctDNA Clearance ACT-treated at 3 months - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",] 
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "POSITIVE" & ctDNA.3months == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.3months == "POSITIVE" ~ 2
  ))

circ_data <- circ_data[circ_data$DFS.3mo.months>=0,]
survfit(Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Clearance from MRD to 3 months ACT-treated | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Clearance", "No Clearance"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2"), labels = c("Clearance", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Fisher test for DFS percentages at 12 and 24 months
dfs_times <- c(12)
circ_data <- na.omit(circ_data[, c("ctDNA.Dynamics", "DFS.3mo.months", "DFS.Event")])
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$ctDNA.Dynamics == "Clearance" & circ_data$DFS.3mo.months >= time & circ_data$DFS.Event == FALSE)
  pos_count <- sum(circ_data$ctDNA.Dynamics == "No Clearance" & circ_data$DFS.3mo.months >= time & circ_data$DFS.Event == FALSE)
  neg_total <- sum(circ_data$ctDNA.Dynamics == "Clearance")
  pos_total <- sum(circ_data$ctDNA.Dynamics == "No Clearance")
  
  neg_surv <- neg_total - sum(circ_data$ctDNA.Dynamics == "Clearance" & circ_data$DFS.Event == TRUE & circ_data$DFS.3mo.months < time)
  pos_surv <- pos_total - sum(circ_data$ctDNA.Dynamics == "No Clearance" & circ_data$DFS.Event == TRUE & circ_data$DFS.3mo.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
```


#DFS by ctDNA Clearance ACT-treated at 6 months - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "POSITIVE" & ctDNA.6months == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.6months == "POSITIVE" ~ 2
  ))

circ_data <- circ_data[circ_data$DFS.6mo.months>=0,]
survfit(Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Clearance from MRD to 6 months ACT-treated | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Clearance", "No Clearance"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2"), labels = c("Clearance", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```


#Percentages of MRD negative with molecular recurrence (returned positive) post-MRD
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "" & circ_data$Lead.Time >= 0, ]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$PostMRDPos.Event=="TRUE",]
circ_datadf <- as.data.frame(circ_data)

# Convert days to months
circ_data$PostMRDPos.months <- circ_data$PostMRDPos/30.437

# Define the intervals: 0-6, 6-9, 9-12, 12-15, 15-18, 18-21, 21-24, >24 months
breaks <- c(0, 6, 9, 12, 15, 18, 21, 24, 48)
labels <- c("0-6m", "6-9m", "9-12m", "12-15m", "15-18m", "18-21m", "21-24m", ">24m")

# Categorize p_drelReturned_months into intervals
circ_data$p_drelReturned_intervals <- cut(circ_data$PostMRDPos.months, breaks = breaks, labels = labels, right = FALSE)

# Examine the distribution of the intervals
table(circ_data$p_drelReturned_intervals)

# Get the counts for each interval
interval_counts <- table(circ_data$p_drelReturned_intervals)

# Calculate the percentages
interval_percentages <- 100 * interval_counts / sum(interval_counts)

# Combine the counts and percentages for a clearer overview
interval_summary <- data.frame(Counts = interval_counts, Percentages = interval_percentages)

# Calculate the total number of observations
total_observations <- sum(interval_counts)

# Add the total number of observations to the summary
interval_summary$TotalObservations <- c(rep(NA, length(interval_counts)-1), total_observations)

# Print the summary with total observations
print(interval_summary)

# Calculate cumulative percentages
cumulative_percentages <- cumsum(interval_percentages)

# Combine the counts, percentages, and cumulative percentages for a clearer overview
interval_summary <- data.frame(Counts = interval_counts, Percentages = interval_percentages, CumulativePercentages = cumulative_percentages, TotalObservations = c(rep(NA, length(interval_counts)-1), total_observations))

bp <- barplot(interval_percentages, 
              main="Molecular Recurrence post-MRD in MRD negative patients", 
              xlab="Months Interval from Surgery", 
              ylab="Patients % converted positive", 
              col="lightblue",
              ylim=c(0, 100))

# Add the cumulative percentages to the plot
points(bp, cumulative_percentages, type="o", pch=22, col="red", cex=1.5)
print(interval_summary)
```




#DFS by ctDNA MRD positive vs ctDNA negative with molecular recurrence at Surveillance - 3 groups
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="NEGATIVE" ~ 1,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="POSITIVE" ~ 2,
    ctDNA.MRD == "POSITIVE" ~ 3
  ))

circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","red"), title="DFS - ctDNA MRD Pos vs Neg with Molecular Recurrence at Surveillance Window", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("All-time negative","Molecular Recurrence", "ctDNA MRD Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3"), labels = c("All-time negative","Molecular Recurrence", "ctDNA MRD Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)

#Fisher test for DFS percentages at 12 and 24 months - All time negative vs Molecular Recurrence
dfs_times <- c(12, 24)
circ_data <- na.omit(circ_data[, c("ctDNA.Dynamics", "DFS.MRD.months", "DFS.Event")])
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$ctDNA.Dynamics == "All-time negative" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
  pos_count <- sum(circ_data$ctDNA.Dynamics == "Molecular Recurrence" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
  neg_total <- sum(circ_data$ctDNA.Dynamics == "All-time negative")
  pos_total <- sum(circ_data$ctDNA.Dynamics == "Molecular Recurrence")
  
  neg_surv <- neg_total - sum(circ_data$ctDNA.Dynamics == "All-time negative" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
  pos_surv <- pos_total - sum(circ_data$ctDNA.Dynamics == "Molecular Recurrence" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)

#Repeat analysis to run Fisher test for All time negative vs ctDNA Positive
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="NEGATIVE" ~ 1,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="POSITIVE" ~ 2,
    ctDNA.MRD == "POSITIVE" ~ 3
  ))

circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]

surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3"), labels = c("All-time negative","Molecular Recurrence", "ctDNA MRD Positive"))

#Fisher test for DFS percentages at 12 and 24 months - All time negative vs ctDNA Positive
dfs_times <- c(12, 24)
circ_data <- na.omit(circ_data[, c("ctDNA.Dynamics", "DFS.MRD.months", "DFS.Event")])
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$ctDNA.Dynamics == "All-time negative" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
  pos_count <- sum(circ_data$ctDNA.Dynamics == "ctDNA MRD Positive" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
  neg_total <- sum(circ_data$ctDNA.Dynamics == "All-time negative")
  pos_total <- sum(circ_data$ctDNA.Dynamics == "ctDNA MRD Positive")
  
  neg_surv <- neg_total - sum(circ_data$ctDNA.Dynamics == "All-time negative" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
  pos_surv <- pos_total - sum(circ_data$ctDNA.Dynamics == "ctDNA MRD Positive" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)

#Repeat analysis to run Fisher test for Molecular Recurrence vs ctDNA Positive
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="NEGATIVE" ~ 1,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="POSITIVE" ~ 2,
    ctDNA.MRD == "POSITIVE" ~ 3
  ))

circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]

surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3"), labels = c("All-time negative","Molecular Recurrence", "ctDNA MRD Positive"))

#Fisher test for DFS percentages at 12 and 24 months - Molecular Recurrence vs ctDNA Positive
dfs_times <- c(12, 24)
circ_data <- na.omit(circ_data[, c("ctDNA.Dynamics", "DFS.MRD.months", "DFS.Event")])
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$ctDNA.Dynamics == "Molecular Recurrence" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
  pos_count <- sum(circ_data$ctDNA.Dynamics == "ctDNA MRD Positive" & circ_data$DFS.MRD.months >= time & circ_data$DFS.Event == 0)
  neg_total <- sum(circ_data$ctDNA.Dynamics == "Molecular Recurrence")
  pos_total <- sum(circ_data$ctDNA.Dynamics == "ctDNA MRD Positive")
  
  neg_surv <- neg_total - sum(circ_data$ctDNA.Dynamics == "Molecular Recurrence" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
  pos_surv <- pos_total - sum(circ_data$ctDNA.Dynamics == "ctDNA MRD Positive" & circ_data$DFS.Event == 1 & circ_data$DFS.MRD.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)

#Repeat to compare HRs for Molecular Recurrence vs ctDNA MRD positive
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="NEGATIVE" ~ 1,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="POSITIVE" ~ 2,
    ctDNA.MRD == "POSITIVE" ~ 3
  ))

circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("2","3"), labels = c("Molecular Recurrence", "ctDNA MRD Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)
```

#DFS by ctDNA at the Surveillance Window - All stages Landmark 10 weeks
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | All stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

#Fisher test for DFS percentages at 12 and 24 months
dfs_times <- c(12, 24)
circ_data <- na.omit(circ_data[, c("ctDNA.Surveillance", "DFS.months", "DFS.Event")])
p_values <- sapply(dfs_times, function(time) {
  neg_count <- sum(circ_data$ctDNA.Surveillance == "NEGATIVE" & circ_data$DFS.months >= time & circ_data$DFS.Event == 0)
  pos_count <- sum(circ_data$ctDNA.Surveillance == "POSITIVE" & circ_data$DFS.months >= time & circ_data$DFS.Event == 0)
  neg_total <- sum(circ_data$ctDNA.Surveillance == "NEGATIVE")
  pos_total <- sum(circ_data$ctDNA.Surveillance == "POSITIVE")
  
  neg_surv <- neg_total - sum(circ_data$ctDNA.Surveillance == "NEGATIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.months < time)
  pos_surv <- pos_total - sum(circ_data$ctDNA.Surveillance == "POSITIVE" & circ_data$DFS.Event == 1 & circ_data$DFS.months < time)
  
  surv_matrix <- matrix(c(neg_surv, pos_surv, neg_total - neg_surv, pos_total - pos_surv), nrow = 2)
  test_result <- fisher.test(surv_matrix)
  return(test_result$p.value)
})
names(p_values) <- paste0("p-value at ", dfs_times, " months")
print(p_values)
```




#Multivariate cox regression at Surveillance Window for DFS - All stages Landmark 10 weeks
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", ">70"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Left-sided colon", "Right-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels = c("0", "1"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$LVI <- factor(circ_data$LVI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No"))
circ_data$PNI <- factor(circ_data$PNI, levels = c("TRUE", "FALSE"), labels = c("Yes", "No"))
circ_data$Grade <- factor(circ_data$Grade, levels = c("G1", "G2/G3"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("Wild-Type", "Mutant"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance + Gender + Age.Group + ECOG + pT + pN + LVI + PNI + Grade + RAS, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for DFS - All Stages", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
```


#DFS by ctDNA Dynamics from MRD to 6 months - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.6mo.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.6months == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.6months == "NEGATIVE" ~ 2,
    ctDNA.MRD == "NEGATIVE" & ctDNA.6months == "POSITIVE" ~ 3,
    ctDNA.MRD == "POSITIVE" & ctDNA.6months == "POSITIVE" ~ 4
  ))

survfit(Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","purple", "red"), title="DFS - ctDNA Dynamics from MRD to 6 months | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3", "4"), labels = c("Persistently Negative", "Converted Negative","Converted Positive", "Persistently Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
```


#Table with recurrence sites by ctDNA at the MRD Window
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]

circ_data_subset1 <- circ_data %>%
  select(
    Rel.Site) %>%
  mutate(
    Rel.Site = factor(Rel.Site, levels = c("Local", "Lymph Node", "Liver", "Lung", "Peritoneum", "Brain")))

circ_data_subset2 <- circ_data %>%
  select(
    Rel.Site,
    ctDNA.MRD) %>%
  mutate(
    Rel.Site = factor(Rel.Site, levels = c("Local", "Lymph Node", "Liver", "Lung", "Peritoneum", "Brain")),
    ctDNA.MRD = factor(ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE")))
Overall <- circ_data_subset1 %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
Overall

ByctDNA_MRD <- circ_data_subset2 %>%
  tbl_summary(
    by = ctDNA.MRD, # add this line to subgroup by ctDNA.MRD
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  add_p() %>%
  bold_labels()
ByctDNA_MRD

merged_table <- tbl_merge(tbls=list(Overall, ByctDNA_MRD))
merged_table

fit1 <- as_flex_table(
  merged_table,
  include = everything(),
  return_calls = FALSE)
fit1
save_as_docx(fit1, path= "~/Downloads/merged_table.docx")
```


#MTM/mL levels at the MRD Window by Radiological Recurrence
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_data <- as.data.frame(circ_data)

# Transform p_MRD_MTM with log10
circ_data$p_MRD_MTM <- as.numeric(as.character(circ_data$p_MRD_MTM))
circ_data$RFS.Event <- factor(circ_data$RFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
summary_stats <- circ_data %>%
  group_by(RFS.Event) %>%
  summarise(
    median_p_MRD_MTM = median(p_MRD_MTM, na.rm = TRUE),
    range_p_MRD_MTM = paste0(min(p_MRD_MTM, na.rm = TRUE), " - ", max(p_MRD_MTM, na.rm = TRUE))
  )
print(summary_stats)

m3_1v2 <- wilcox.test(p_MRD_MTM ~ RFS.Event,
                      data = circ_data[circ_data$RFS.Event %in% c("No Recurrence", "Recurrence"), ],
                      na.rm = TRUE)
print(m3_1v2)
boxplot(p_MRD_MTM~RFS.Event, data=circ_data, main="ctDNA MRD Window MTM - Recurrence", xlab="Recurrence", ylab="MTM/mL", col="white",border="black", ylim = c(0, 50))
```

#MTM/mL levels at the MRD Window by Radiological Recurrence Sites
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_data <- as.data.frame(circ_data)

# Transform p_MRD_MTM with log10
circ_data$p_MRD_MTM <- as.numeric(as.character(circ_data$p_MRD_MTM))
circ_data$Rel.Site <- factor(circ_data$Rel.Site, levels = c("Liver", "Lymph Node", "Local", "Peritoneum", "Lung", "Brain"))
summary_stats <- circ_data %>%
  group_by(Rel.Site) %>%
  summarise(
    median_p_MRD_MTM = median(p_MRD_MTM, na.rm = TRUE),
    range_p_MRD_MTM = paste0(min(p_MRD_MTM, na.rm = TRUE), " - ", max(p_MRD_MTM, na.rm = TRUE))
  )
print(summary_stats)
kruskal_test <- kruskal.test(p_MRD_MTM ~ Rel.Site, data = circ_data)
print(kruskal_test)
boxplot(p_MRD_MTM~Rel.Site, data=circ_data, main="ctDNA MRD Window MTM - Recurrence Site", xlab="Recurrence Site", ylab="MTM/mL", col="white",border="black", ylim = c(0, 40))
```

#Barplot with Recurrence Sites (Liver vs Others) by ctDNA at the MRD Window
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Rel.Site <- factor(circ_data$Rel.Site, levels = c("Liver", "Lymph Node", "Local", "Peritoneum", "Lung", "Brain"))
contingency_table <- table(circ_data$Rel.Site, circ_data$ctDNA.MRD)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "Patients with Radiological Recurrence", 
       x = "Recurrence Site", 
       y = "Patients (%)", 
       fill = "ctDNA at MRD",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Negative" = "blue", "Positive" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```

#Detection ctDNA rates based on sites of relapse
```{r}
# Remove existing objects and set the working directory
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$CohortB=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]

# Create a table of counts for the "Rel.Site" variable
relsite_counts <- table(circ_data$Rel.Site)
relsite_df <- as.data.frame(relsite_counts)
names(relsite_df) <- c("Rel.Site", "Count")
circ_data_pos_mrd <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data_pos_anytime <- circ_data[circ_data$ctDNA.anytime=="POSITIVE",]
pos_counts_mrd <- table(circ_data_pos_mrd$Rel.Site)
pos_counts_anytime <- table(circ_data_pos_anytime$Rel.Site)
relsite_df$MRDPos_Count <- ifelse(is.na(match(relsite_df$Rel.Site, names(pos_counts_mrd))), 0, pos_counts_mrd[match(relsite_df$Rel.Site, names(pos_counts_mrd))])
relsite_df$MRDPos_Count[is.na(relsite_df$MRDPos_Count)] <- 0
relsite_df$AnytimePos_Count <- ifelse(is.na(match(relsite_df$Rel.Site, names(pos_counts_anytime))), 0, pos_counts_anytime[match(relsite_df$Rel.Site, names(pos_counts_anytime))])
relsite_df$AnytimePos_Count[is.na(relsite_df$AnytimePos_Count)] <- 0
relsite_df$Percent <- (relsite_df$Count / sum(relsite_df$Count)) * 100
relsite_df$MRDPos_Percent <- (relsite_df$MRDPos_Count / relsite_df$Count) * 100
relsite_df$AnytimePos_Percent <- (relsite_df$AnytimePos_Count / relsite_df$Count) * 100
total_observations <- sum(relsite_df$Count)
total_pos_mrd <- sum(relsite_df$MRDPos_Count)
total_pos_anytime <- sum(relsite_df$AnytimePos_Count)
total_row <- data.frame(Rel.Site = "Total", Count = total_observations, MRDPos_Count = total_pos_mrd, AnytimePos_Count = total_pos_anytime, Percent = 100, MRDPos_Percent = (total_pos_mrd / total_observations) * 100, AnytimePos_Percent = (total_pos_anytime / total_observations) * 100)
relsite_df <- rbind(relsite_df, total_row)
print(relsite_df)

ft <- flextable(relsite_df)
doc <- read_docx() %>%
  body_add_flextable(value = ft)
print(doc, target = "relsite_df.docx")
```

